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""" Dataset reader for webdataset
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import json
import logging
import math
import os
import random
import sys
from dataclasses import dataclass
from functools import partial
from itertools import islice
from typing import Any, Callable, Dict, List, Optional, Tuple
import torch
import torch.distributed as dist
import yaml
from PIL import Image
from torch.utils.data import Dataset, IterableDataset, get_worker_info
try:
import webdataset as wds
from webdataset.filters import _shuffle, getfirst
from webdataset.shardlists import expand_urls
from webdataset.tariterators import base_plus_ext, url_opener, tar_file_expander, valid_sample
except ImportError:
wds = None
expand_urls = None
from .class_map import load_class_map
from .reader import Reader
from .shared_count import SharedCount
_logger = logging.getLogger(__name__)
SAMPLE_SHUFFLE_SIZE = int(os.environ.get('WDS_SHUFFLE_SIZE', 8192))
SAMPLE_INITIAL_SIZE = int(os.environ.get('WDS_INITIAL_SIZE', 2048))
def _load_info(root, names=('_info.json', 'info.json')):
if isinstance(names, str):
names = (names,)
tried = []
err_str = ''
for n in names:
full_path = os.path.join(root, n)
try:
tried.append(full_path)
with wds.gopen(full_path) as f:
if n.endswith('.json'):
info_dict = json.load(f)
else:
info_dict = yaml.safe_load(f)
return info_dict
except Exception as e:
err_str = str(e)
_logger.warning(
f'Dataset info file not found at {tried}. Error: {err_str}. '
'Falling back to provided split and size arg.')
return {}
@dataclass
class SplitInfo:
num_samples: int
filenames: Tuple[str]
shard_lengths: Tuple[int] = ()
alt_label: str = ''
name: str = ''
def _parse_split_info(split: str, info: Dict):
def _info_convert(dict_info):
return SplitInfo(
num_samples=dict_info['num_samples'],
filenames=tuple(dict_info['filenames']),
shard_lengths=tuple(dict_info['shard_lengths']),
alt_label=dict_info.get('alt_label', ''),
name=dict_info['name'],
)
if 'tar' in split or '..' in split:
# split in WDS string braceexpand format, sample count can be included with a | separator
# ex: `dataset-split-{0000..9999}.tar|100000` for 9999 shards, covering 100,000 samples
split = split.split('|')
num_samples = 0
split_name = ''
if len(split) > 1:
num_samples = int(split[1])
split = split[0]
if '::' not in split:
split_parts = split.split('-', 3)
split_idx = len(split_parts) - 1
if split_idx and 'splits' in info and split_parts[split_idx] in info['splits']:
split_name = split_parts[split_idx]
split_filenames = expand_urls(split)
if split_name:
split_info = info['splits'][split_name]
if not num_samples:
_fc = {f: c for f, c in zip(split_info['filenames'], split_info['shard_lengths'])}
num_samples = sum(_fc[f] for f in split_filenames)
split_info['filenames'] = tuple(_fc.keys())
split_info['shard_lengths'] = tuple(_fc.values())
split_info['num_samples'] = num_samples
split_info = _info_convert(split_info)
else:
split_info = SplitInfo(
name=split_name,
num_samples=num_samples,
filenames=split_filenames,
)
else:
if 'splits' not in info or split not in info['splits']:
raise RuntimeError(f"split {split} not found in info ({info.get('splits', {}).keys()})")
split = split
split_info = info['splits'][split]
split_info = _info_convert(split_info)
return split_info
def log_and_continue(exn):
"""Call in an exception handler to ignore exceptions, isssue a warning, and continue."""
_logger.warning(f'Handling webdataset error ({repr(exn)}). Ignoring.')
# NOTE: try force an exit on errors that are clearly code / config and not transient
if isinstance(exn, TypeError):
raise exn
return True
def _decode(
sample,
image_key='jpg',
image_mode='RGB',
target_key='cls',
alt_label=''
):
""" Custom sample decode
* decode and convert PIL Image
* cls byte string label to int
* pass through JSON byte string (if it exists) without parse
"""
# decode class label, skip if alternate label not valid
if alt_label:
# alternative labels are encoded in json metadata
meta = json.loads(sample['json'])
class_label = int(meta[alt_label])
if class_label < 0:
# skipped labels currently encoded as -1, may change to a null/None value
return None
else:
class_label = int(sample[target_key])
# decode image
img = getfirst(sample, image_key)
with io.BytesIO(img) as b:
img = Image.open(b)
img.load()
if image_mode:
img = img.convert(image_mode)
# json passed through in undecoded state
decoded = dict(jpg=img, cls=class_label, json=sample.get('json', None))
return decoded
def pytorch_worker_seed():
"""get dataloader worker seed from pytorch"""
worker_info = get_worker_info()
if worker_info is not None:
# favour the seed already created for pytorch dataloader workers if it exists
return worker_info.seed
# fallback to wds rank based seed
return wds.utils.pytorch_worker_seed()
if wds is not None:
# conditional to avoid mandatory wds import (via inheritance of wds.PipelineStage)
class detshuffle2(wds.PipelineStage):
def __init__(
self,
bufsize=1000,
initial=100,
seed=0,
epoch=-1,
):
self.bufsize = bufsize
self.initial = initial
self.seed = seed
self.epoch = epoch
def run(self, src):
if isinstance(self.epoch, SharedCount):
epoch = self.epoch.value
else:
# NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
# situation as different workers may wrap at different times (or not at all).
self.epoch += 1
epoch = self.epoch
if self.seed < 0:
seed = pytorch_worker_seed() + epoch
else:
seed = self.seed + epoch
# _logger.info(f'shuffle seed: {self.seed}, {seed}, epoch: {epoch}') # FIXME temporary
rng = random.Random(seed)
return _shuffle(src, self.bufsize, self.initial, rng)
else:
detshuffle2 = None
class ResampledShards2(IterableDataset):
"""An iterable dataset yielding a list of urls."""
def __init__(
self,
urls,
nshards=sys.maxsize,
worker_seed=None,
deterministic=True,
epoch=-1,
):
"""Sample shards from the shard list with replacement.
:param urls: a list of URLs as a Python list or brace notation string
"""
super().__init__()
urls = wds.shardlists.expand_urls(urls)
self.urls = urls
assert isinstance(self.urls[0], str)
self.nshards = nshards
self.rng = random.Random()
self.worker_seed = pytorch_worker_seed if worker_seed is None else worker_seed
self.deterministic = deterministic
self.epoch = epoch
def __iter__(self):
"""Return an iterator over the shards."""
if isinstance(self.epoch, SharedCount):
epoch = self.epoch.value
else:
# NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
# situation as different workers may wrap at different times (or not at all).
self.epoch += 1
epoch = self.epoch
if self.deterministic:
# reset seed w/ epoch if deterministic, worker seed should be deterministic due to arg.seed
self.rng = random.Random(self.worker_seed() + epoch)
for _ in range(self.nshards):
index = self.rng.randint(0, len(self.urls) - 1)
yield dict(url=self.urls[index])
class ReaderWds(Reader):
def __init__(
self,
root: str,
name: Optional[str] = None,
split: str = 'train',
is_training: bool = False,
num_samples: Optional[int] = None,
batch_size: int = 1,
repeats: int = 0,
seed: int = 42,
class_map: Optional[dict] = None,
input_key: str = 'jpg;png;webp',
input_img_mode: str = 'RGB',
target_key: str = 'cls',
target_img_mode: str = '',
filename_key: str = 'filename',
sample_shuffle_size: Optional[int] = None,
smaple_initial_size: Optional[int] = None,
):
super().__init__()
if wds is None:
raise RuntimeError(
'Please install webdataset 0.2.x package `pip install git+https://github.com/webdataset/webdataset`.')
self.root = root
self.is_training = is_training
self.batch_size = batch_size
self.repeats = repeats
self.common_seed = seed # a seed that's fixed across all worker / distributed instances
self.shard_shuffle_size = 500
self.sample_shuffle_size = sample_shuffle_size or SAMPLE_SHUFFLE_SIZE
self.sample_initial_size = smaple_initial_size or SAMPLE_INITIAL_SIZE
self.input_key = input_key
self.input_img_mode = input_img_mode
self.target_key = target_key
self.filename_key = filename_key
self.key_ext = '.JPEG' # extension to add to key for original filenames (DS specific, default ImageNet)
self.info = _load_info(self.root)
self.split_info = _parse_split_info(split, self.info)
if num_samples is not None:
self.num_samples = num_samples
else:
self.num_samples = self.split_info.num_samples
if is_training and not self.num_samples:
raise RuntimeError(f'Invalid split definition, num_samples not specified in train mode.')
self.remap_class = False
if class_map:
self.class_to_idx = load_class_map(class_map)
self.remap_class = True
else:
self.class_to_idx = {}
# Distributed world state
self.dist_rank = 0
self.dist_num_replicas = 1
if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1:
self.dist_rank = dist.get_rank()
self.dist_num_replicas = dist.get_world_size()
# Attributes that are updated in _lazy_init
self.worker_info = None
self.worker_id = 0
self.worker_seed = seed # seed unique to each worker instance
self.num_workers = 1
self.global_worker_id = 0
self.global_num_workers = 1
self.init_count = 0
self.epoch_count = SharedCount()
# DataPipeline is lazy init, the majority of WDS DataPipeline could be init here, BUT, shuffle seed
# is not handled in manner where it can be deterministic for each worker AND initialized up front
self.ds = None
def set_epoch(self, count):
self.epoch_count.value = count
def set_loader_cfg(
self,
num_workers: Optional[int] = None,
):
if self.ds is not None:
return
if num_workers is not None:
self.num_workers = num_workers
self.global_num_workers = self.dist_num_replicas * self.num_workers
def _lazy_init(self):
""" Lazily initialize worker (in worker processes)
"""
if self.worker_info is None:
worker_info = torch.utils.data.get_worker_info()
if worker_info is not None:
self.worker_info = worker_info
self.worker_id = worker_info.id
self.worker_seed = worker_info.seed
self.num_workers = worker_info.num_workers
self.global_num_workers = self.dist_num_replicas * self.num_workers
self.global_worker_id = self.dist_rank * self.num_workers + self.worker_id
# init data pipeline
abs_shard_filenames = [os.path.join(self.root, f) for f in self.split_info.filenames]
pipeline = [wds.SimpleShardList(abs_shard_filenames)]
# at this point we have an iterator over all the shards
if self.is_training:
pipeline.extend([
detshuffle2(
self.shard_shuffle_size,
seed=self.common_seed,
epoch=self.epoch_count,
),
self._split_by_node_and_worker,
# at this point, we have an iterator over the shards assigned to each worker
wds.tarfile_to_samples(handler=log_and_continue),
wds.shuffle(
bufsize=self.sample_shuffle_size,
initial=self.sample_initial_size,
rng=random.Random(self.worker_seed) # this is why we lazy-init whole DataPipeline
),
])
else:
pipeline.extend([
self._split_by_node_and_worker,
# at this point, we have an iterator over the shards assigned to each worker
wds.tarfile_to_samples(handler=log_and_continue),
])
pipeline.extend([
wds.map(
partial(
_decode,
image_key=self.input_key,
image_mode=self.input_img_mode,
alt_label=self.split_info.alt_label,
),
handler=log_and_continue,
),
wds.rename(image=self.input_key, target=self.target_key)
])
self.ds = wds.DataPipeline(*pipeline)
def _split_by_node_and_worker(self, src):
if self.global_num_workers > 1:
for s in islice(src, self.global_worker_id, None, self.global_num_workers):
yield s
else:
for s in src:
yield s
def _num_samples_per_worker(self):
num_worker_samples = self.num_samples / max(self.global_num_workers, self.dist_num_replicas)
if self.is_training or self.dist_num_replicas > 1:
num_worker_samples = math.ceil(num_worker_samples)
if self.is_training:
num_worker_samples = math.ceil(num_worker_samples / self.batch_size) * self.batch_size
return int(num_worker_samples)
def __iter__(self):
if self.ds is None:
self._lazy_init()
num_worker_samples = self._num_samples_per_worker()
if self.is_training or self.dist_num_replicas > 1:
# NOTE: doing distributed validation w/ WDS is messy, hard to meet constraints that
# same # of batches needed across all replicas w/ seeing each sample once.
# with_epoch() is simple but could miss a shard's worth of samples in some workers,
# and duplicate in others. Best to keep num DL workers low and a divisor of #val shards.
ds = self.ds.with_epoch(num_worker_samples)
else:
ds = self.ds
i = 0
# _logger.info(f'start {i}, {self.worker_id}') # FIXME temporary debug
for sample in ds:
target = sample['target']
if self.remap_class:
target = self.class_to_idx[target]
yield sample['image'], target
i += 1
# _logger.info(f'end {i}, {self.worker_id}') # FIXME temporary debug
def __len__(self):
num_samples = self._num_samples_per_worker() * self.num_workers
return num_samples
def _filename(self, index, basename=False, absolute=False):
assert False, "Not supported" # no random access to examples
def filenames(self, basename=False, absolute=False):
""" Return all filenames in dataset, overrides base"""
if self.ds is None:
self._lazy_init()
names = []
for sample in self.ds:
if self.filename_key in sample:
name = sample[self.filename_key]
elif '__key__' in sample:
name = sample['__key__'] + self.key_ext
else:
assert False, "No supported name field present"
names.append(name)
if len(names) >= self.num_samples:
break # safety for ds.repeat() case
return names
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